Field of the Invention
The present invention is in the field of use of computer systems in business information management, operations and predictive planning. Specifically, the use of a highly scalable, distributed, and self-load balancing connection interface programmed to capture information from a wide range of network service sources and then format that information for tightly specified downstream business information system uses.
Discussion of the State of the Art
Over the past decade, the amount of financial, operational, infrastructure, risk management and philosophical information available to decision makers of a business from such sources as ubiquitous sensors found on a business's equipment or available from third party sources, detailed cause and effect data, and business process monitoring software has expanded to the point where the data has overwhelmed corporate executives' abilities to follow all of it and certainly to interpret and make meaningful use of that available data in a given business environment. In other words, the torrent of business related information now available to a corporate decision maker or group of decision makers has far outgrown the ability of those in most need of its use to either fully follow it or reliably use it. Failure to recognize important trends or become aware of information in a timely fashion has led to highly visible, customer facing, outages at NETFLIX™, FACEBOOK™, and UPS™ over the past few years, just to list a few.
There have been several developments in business software that have arisen with the purpose of streamlining or automating either business data analysis or business decision process. PLANATIR™ offers software to isolate patterns in large volumes of data, DATABRICKS™ offers custom analytics services, ANAPLAN™ offers financial impact calculation services and there are other software sources that mitigate some aspect of business data relevancy identification, analysis of that data and business decision automation, but none of these solutions handle more than a single aspect of the whole task. This insinuates the technology being used in the decision process as one of the variables as data from one software package often must be significantly and manually transformed to be introduced into the software for the next analysis, if appropriate software exists. This step is both inefficient use of human resources and has potential to introduce error at a critical process point.
There has also been a great proliferation in the use of network based service companies offering solutions for such business functions as customer relationship management, world event news sourcing, market news sourcing and analysis, infrastructure monitoring, human resource management, business real estate conditions, and government activity information sourcing from the world. This only serves to add to the overload of information described above, and, to be of use, must be carefully analyzed by any business information management system purporting to provide reliable predictions. A robust connection interface to all network service sources of business interest must be provided.
Currently, there are a small number of scriptable data capture and sort interfaces such as: Zapier and IFTTT, both able to connect to a number of network data sources. However, these offerings possess only very lightweight logic options for moving the captured data into specific categories or transformation pathways which greatly limit their usefulness in complex business situations often encountered. Another, Open Source, capture engine, Sparkta is focused on streaming aggregation and fails to provide flexibility for routinely supporting event-driven polling, in addition to, passive stream monitoring of third-party APIs and similar operations needed by a business operating system.
What is needed is a fully integrated system that retrieves business relevant information from many disparate and heterogeneous sources using a scalable, expressively scriptable, connection interface, identifies and analyzes that high volume data, transforming it to a business useful format and then uses that data to drive an integrated highly scalable simulation engine which may employ combinations of the system dynamics, discrete event and agent based paradigms within a simulation run such that the most useful and accurate data is obtained and stored for the needs of the analyst. This multimethod information capture, analysis, transformation and outcome prediction system forming a “business operating system.”